Method and device for automatically checking the quality of an rr series obtained from a cardiac signal

ABSTRACT

The method allows controlling the quality of an initial RR series consisting of a plurality of (RRi) samples which are respectively a function of time intervals (δti) which separate two successive heartbeats. During this method, one resamples the RR series so as to obtain a resampled RR series, and one automatically controls the quality of the RR series by automatically calculating at least the mathematical norm value (NORME), in a sliding window, of the resampled RR series, said mathematical norm value being given by the following formula: 
     
       
         
           
             NORME 
             = 
             
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   N 
                 
                  
                 
                   
                     ( 
                     
                       
                         RR 
                         i 
                       
                       - 
                       
                         
                           1 
                           N 
                         
                          
                         
                           
                             ∑ 
                             
                               i 
                               = 
                               1 
                             
                             N 
                           
                            
                           
                             ( 
                             
                               RR 
                               i 
                             
                             ) 
                           
                         
                       
                     
                     ) 
                   
                   2 
                 
               
             
           
         
       
     
     where N is the number of RRi samples in said window.

FIELD OF THE INVENTION

The present invention relates to the field of digital processing of abioelectrical signal, which is characteristic of the cardiac rhythm of aliving being, and which is designated in the present text by the termcardiac signal. This is for example, but not exclusively, anelectrocardiographic (ECG) signal. In this technical field, theinvention relates to automatic quality control of an RR series obtainedby sampling a cardiac signal.

PRIOR ART

From a physiological point of view, the heart of a living being,isolated from outside influence, contracts automatically and veryregularly as does a metronome, under the action of the sinus node whichgenerates an independent nerve impulse and, thereby, causes aspontaneous cardiac muscle contraction. The heart is not howeverisolated, but is connected to the autonomic nervous system (ANS) viaparasympathetic and sympathetic systems. The autonomic nervous systeminfluences the activity of the heart: the sympathetic system acceleratesthe heart rate, while the parasympathetic system slows it down. Thus,despite a certain degree of autonomy, the heart undergoes influencesfrom the autonomic nervous system, which allows, in particular, the bodyof a living being to adapt the heart rate depending on its needs,however within reasonable limits. It is understood, therefore, that theanalysis of the evolution of the heart rate over time, and in particularchanges in the heart rate (changes in the heart beat), providesimportant information on the activity of the cardiac system,particularly on the activity of the autonomic nervous system. Now,knowledge of ANS activity can be of great help in the development of adiagnosis of many clinical situations. On this subject, reference may bemade, for example, to the following publication: Lacroix D, Logier R.,Kacet S., Hazard J-R, Dagano J. (1992): “Effects of consecutiveadministration of central and peripheral anticholinergic agents onrespiratory sinus arrhythmia in normal subjects, J. of the AutonomicNervous System”, Vol 39, pages 211-218.

To study these fluctuations in heart rate, various filtering techniquesand spectral analysis of a signal representing the evolution over timeof the instant heart rate (or frequency) have already been developedsince 1970, such signal which is obtained after sampling an analogbioelectrical signal characteristic of the heartbeat of a living being,and termed afterwards “analog cardiac signal.”

To acquire this cardiac signal, different techniques of invasive ornon-invasive acquisition are known. One known invasive technique is, forexample, to use a blood pressure sensor connected to a catheter insertedinto an artery. Among the known non-invasive methods are included, forexample, the use of an infrared pulse sensor, or the acquisition of anelectrocardiographic (ECG) signal using an electrocardiograph. Thislatter method of acquiring an ECG signal is in practice the mostcommonly used to date, because, in addition to its noninvasive nature,it advantageously provides a more accurate signal than that which isobtained, for example, by means of an infrared pulse sensor.

The ECG signal is known as consisting of a succession of electricaldepolarizations whose appearance is shown in FIG. 3 attached. The Pwave, which corresponds to the depolarization of the atria, has a lowamplitude and a dome shape. The PQ space reflects the time ofatrioventricular conduction. The QRS complex reflects the ventricularcontraction, and the T wave the ventricular repolarization. In practice,the peak R is considered as a marker of the ventricular systole, that isto say, of the heart beat.

In practice, the R wave usually being the finest and most extensive partof the QRS, it is generally used to locate the heart beat with very goodaccuracy, in practice of the order of one thousandth of a second. Thus,the time interval between two successive R waves accuratelycharacterizes the time separating two successive heartbeats; this is theperiod of the ECG signal, and the inverse of this period gives theinstantaneous heart rate.

To automatically construct the signal, called afterwards the RR series,representing the evolution in time of the instantaneous heart rate, theECG signal, which is an analog signal (analog/digital conversion of theECG signal), is sampled, and the sampled digital ECG signal is processedby automatically detecting R waves in this digital signal. An RR seriesis thus, in a conventional manner, comprised of a plurality ofsuccessive RRi samples (or points), each RRi sample being a function ofthe time interval between two successive R waves of the ECG signal.

However, it should be noted, on the one hand, that the other waves ofdepolarization (P, Q, S or T) of the ECG signal can also be used forcharacterizing the heart rate, even if the measurement accuracy is notas good as when using the R waves. On the other hand, depending on theacquisition technique chosen, the cardiac signal may have a differentshape from that of the above mentioned ECG signal. The cardiac signal isnot necessarily analog, but may be a digital signal. Accordingly, in thepresent text, the term RR series is not limited to the aforementionedspecific definition based on the R waves of an ECG signal, but isdefined in a more general way in the context of the present invention asa series of several digital samples called RR, obtained from a cardiacsignal that is characteristic of the heart rate, each RRi sample being afunction of the time interval between two successive heartbeats. Each RRsample may be proportional, and in particular equal, to the timeinterval between two successive heartbeats, or inversely proportional tothe time interval between two successive heartbeats.

In practice, disturbances in the cardiac signal, especially in an ECGsignal, induce, in the RR series issued from this cardiac signal, abruptchanges of short duration, commonly called artifacts.

Disturbances, causing artifacts in the RR series, may be physiologicaland intrinsically linked to a temporary malfunction of the cardiacsystem; it may be, for example, an extrasystole. These disturbances mayalso be external and not related to the functioning of the cardiacsystem; it may be, for example, due to a patient's movement, brieflyaltering the measurement signal.

Artifacts in an RR series may result in a single incorrect sample or ina plurality of successive incorrect samples. In practice, an artifact inthe RR series can be likened to a Dirac pulse, and is reflected, in thefrequency domain, by a rectangular continuous broadband spectrum.Therefore, assuming that a series RR could be transposed in thefrequency domain (by Fourier transform or other), without first takingspecial precautions, the presence of artifacts in the RR series wouldresult in the frequency domain by obtaining a very disturbed frequencyspectrum of the RR series, of rectangular broadband shape, masking thespectrum of the real signal.

For this reason, to get correct frequency information, it is essentialto eliminate the artifacts before performing the frequencytransposition.

It was thus proposed, in international patent application WO 02/069178,as well as in the article from Logier R, De Jonckheere J, DassonnevilleA., “An efficient algorithm for R-R intervals series filtering”. ConfProc IEEE Eng Med Biol Soc. 2004; 6:3937-40, digital filteringalgorithms, which generally allow filtering in real time a series RRobtained from a cardiac signal, by automatically detecting in the RRseries the presence of one or more successive incorrect RR_(i) samples,and by automatically replacing in the RR series the incorrect RRisamples that were detected by corrected RR_(c) samples. Detectingincorrect RR samples may be performed in various ways and the corrected(RR_(c)) samples may also be calculated in various ways, and forexample, and for example, but not exclusively, by linear interpolation.

A problem of these filtering algorithms, designated subsequentlyalgorithms or filtering method “with reconstruction of incorrect samplesof an RR series”, lies in the fact that the reconstruction of the RRseries by replacing incorrect RR_(i) samples which were detected bycorrected RR_(c) samples, can result in a final RR series partly rebuiltwhich is itself partially or completely distorted, especially when thecardiac signal that was taken is of poor quality. The lack of quality ofthis cardiac signal may be the result of many factors, such as, forexample, and in a non-limiting, non-exhaustive manner, poor positioningof the electrodes or sensors of the heart signal, insufficient signalamplification in the signal processing chain, etc. . . . .

But the reconstruction of a distorted RR series has not so far beendetected by the filtering algorithms in a series RR. It follows that theinformation provided by these filtering algorithms can be completelywrong or insignificant without anyone noticing.

PURPOSE OF THE INVENTION

The present invention aims at providing a solution for an automaticquality control of an RR series obtained from a cardiac signal.

SUMMARY OF THE INVENTION

The first purpose of the invention is thus a quality control method ofan initial RR series consisting of a plurality of samples (RR_(i)) whichare respectively a function of time intervals (δti) which separate twosuccessive heartbeats, method during which one resamples the RR seriesso as to obtain a resampled RR series. Characteristically, according tothe invention, one automatically controls the quality of the RR seriesby automatically calculating at least the mathematical norm value, in asliding window, of the resampled RR series, said mathematical norm valuebeing given by the following formula:

${NORME} = \sqrt{\sum\limits_{i = 1}^{N}\left( {{RR}_{i} - {\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {RR}_{i} \right)}}} \right)^{2}}$

where N is the number of RR_(i) samples in said window.

In this text, and particularly in the claims, the term “cardiac signal”means any physical signal characteristic of the instantaneous heart rate(or frequency) of a living being. For the implementation of theinvention, various invasive or non-invasive techniques can be used toacquire the cardiac signal. One known invasive technique consists, forexample, in using a blood pressure sensor connected to a catheterinserted into an artery. Among the known non-invasive methods (and whichis preferable) are, for example, one which consists in using an infraredpulse sensor, using an ultrasonic sensor for detection of the cardiaccycles, the type of sensor implemented in a carditocograph, or theacquisition of an electrocardiographic (ECG) signal. The acquisition ofan electrocardiographic (ECG) signal is in practice the most commonlyused method, because besides its noninvasive nature, it provides a moreaccurate cardiac signal than that obtained, for example, by means of aninfrared pulse sensor.

In this text, and particularly in the claims, the term “RR series”generally means a series of various successive samples RR obtained froma cardiac signal characteristic of the cardiac rhythm of a living being,each RR_(i) sample being generally based on a time interval (δti)between two successive heartbeats. Generally, each sample (RR_(i)) isproportional, in particular equal, to the time interval (δti) betweentwo successive heartbeats. Each (RR_(i)) sample may also beproportional, and more particularly equal to the inverse (1/δti) of thetime interval between two successive heartbeats.

In the preferred exemplary embodiment described below with reference tothe accompanying figures, the RR series is more particularly constructedfrom the R waves of an ECG signal. This is not, however, limiting theinvention. In the case of an ECG type cardiac signal, one can build theseries called “RR” using the other depolarization waves (P, Q, S or T)of the ECG signal to construct the RR series, the accuracy not beinghowever as good as when using the R waves of the ECG signal. Also, whenthe cardiac signal is not an ECG signal, the samples of the RR seriesare not calculated by determining the time interval (δti) separating twosuccessive R waves of the ECG signal, but are, more generally,determined by detecting in the cardiac signal the time interval betweentwo successive heartbeats.

The invention also relates to a device for processing an RR seriesconsisting of a plurality of (RRi) samples which are respectively afunction of the time intervals (δti) separating two successiveheartbeats, said device being designed to automatically control thequality of the RR series by implementing the aforementioned method.

Another purpose of the invention is an acquisition and processing systemof a cardiac signal, said system comprising electronic acquisition meansof a cardiac signal, and electronic processing means designed forconstructing an initial RR series from the cardiac signal acquired bythe electronic acquisition means, said RR series consisting of aplurality of samples (RR_(i)) which are respectively a function of thetime intervals (δti) separating two successive heartbeats of the cardiacsignal. Typically according to the invention, said electronic processingmeans are designed to automatically control the quality of an RR seriesby implementing the aforementioned method.

The invention also provides a computer program comprising means forcoding a computer program adapted to be executed by electronicprocessing means, and, when executed by electronic processing means, forimplementing the aforementioned quality control method of an RR series.

BRIEF DESCRIPTION OF FIGURES

Other features and advantages of the invention will appear more clearlyupon reading the detailed description below of a preferred embodiment ofthe method of the invention, said detailed description being given byway of nonlimiting and non-exhaustive example, with reference to theaccompanying drawings in which:

FIG. 1 schematically represents the main elements of an exemplaryacquisition and processing system of an ECG signal implementing themethod of the invention,

FIG. 2 represents the set of waves (PQRST) characteristic of a cardiacbeat in an ECG signal,

FIG. 3 shows an example of digital ECG signal obtained after sampling ananalog ECG signal,

FIG. 4 shows an example of an RR series (still designated as RR signal)constructed from the signal of FIG. 3.

DETAILED DESCRIPTION System for Acquiring and Processing the CardiacSignal

FIG. 1 shows an example of an acquisition and processing system of thecardiac signal of a living being (human or animal) that is used for theimplementation of the method according to the invention. This systemcomprises:

-   -   conventional electronic means for acquiring an ECG signal,        comprising several measuring electrodes 1 connected at their        input to an electrocardiographic (ECG) monitor 2,    -   electronic means 3 for processing the ECG signal outputted by        the ECG monitor 2.

The processing means 3 of the ECG signal comprises an analog/digitalconverter 30, and an electronic processing unit 31. The input ofconverter 30 is connected to the output of the ECG monitor 2, and theoutput of the converter 30 is connected to an input port of theelectronic processing unit 31. In one particular non-limiting embodimentof the invention, the processing unit 31 is constituted by amicrocomputer, the converter 30 being connected to a serial port RS232of this microcomputer. The invention is not limited to theimplementation of a microcomputer as the electronic processing unit 31can be implemented differently, for example as an FPGA type programmableelectronic circuit, or as an integrated ASIC type circuit.

In operation, the electrodes 1 are applied to the body of the livingbeing, and the ECG monitor 2 outputs in the usual way an analogelectrical signal, called ECG signal, that has the shape of the signalshown in FIG. 2 for each heart beat.

Referring to FIG. 2, for each heart beat, this electrocardiographic(ECG) signal consists of a set of electric waves:

-   -   the P wave, which corresponds to the depolarization of the        atria, and which has a small amplitude and a dome shape;    -   the PQ space which reflects the time of atrioventricular        conduction;    -   the R wave, regarded in practice as a marker of ventricular        systole, or the heart beat, the QRS complex reflecting        ventricular contraction, and    -   the T wave which reflects ventricular repolarization.

This analog ECG signal is digitized by the converter 4 with apredetermined sampling frequency (fc), equal for example to 256 Hz.

The sampled signal output from the converter 30 (signal shown in FIG. 3)is processed by the processing unit 31 by means of specific processingsoftware (filtering software) which is described in detail below. Thisfiltering software is stored in memory of the processing unit 31 andallows, when executed, automatically constructing, from the digitalsignal delivered by the analog/digital converter 30, an RR series with,optionally, automatic reconstruction of incorrect samples RRi, andautomatically calculating a NivQual quality index that can control thequality of the RR series, optionally partly reconstructed.

A preferred variant of this filtering software will now be detailed.

Example of Filtering Software Algorithm

In a particular variant embodiment of the invention, the main successivesteps of the filtering algorithm are the following:

-   -   1. Acquisition and construction of RRi samples from the signal        output from the analog/digital converter 30.    -   2. Filtering the RR series with optional automatic detection of        incorrect samples RRi, and substituting with reconstructed        samples identified in the RRc samples series.    -   3. Re-sampling of the RR series to a predefined frequency f to        obtain resampled RRi samples.    -   4. Selection of RRi samples included in a time window of n        seconds (n>1/f).    -   5. Calculating a NivQual quality index    -   6. Offsetting, with a time step equal to p seconds (preferably        p≦n), the time window of n seconds, and reiterating the        calculation from step 2. This offset corresponds to the sliding        of the time window for selecting the samples.

In practice, the system can be programmed to be used in real time ordelayed time.

When the system is used in delayed time, step 1 is performed first inreal time so as to build all RRi samples over all the period of analysisdesired; all of these successive RRi samples are stored in memory, forexample in a memory acquisition file of the processing unit 31.Secondly, the steps 2-6 are performed in a loop, offline, on the RRisamples stored in the acquisition file.

When the system operates in real time, step 1 of construction of the RRisamples on the one hand, and the other processing steps 2-6 on the otherhand, are performed by two separate software modules operating inparallel, the first construction module (step 1) supplying the secondprocessing and calculation module (steps 2-6) for example through abuffer file or register or equivalent.

Steps 1-5 will now be detailed.

Step 1: Acquisition and Construction of RRi Samples

The acquisition and construction of the RRi samples are performed by afirst software sub-module which is input with the successive digitaldata constituting the digitized ECG signal (signal of FIG. 3) output bythe analog digital converter 30. Each data (or point) of the ECG signalis determined by the instantaneous amplitude ECGi of the ECG signal, andby sampling time t_(i) (t_(i)=n_(i)/fc, with ni being the sample numberand fc representing the sampling frequency of the converter 30).

The first acquisition sub-module of RRi samples is designed toautomatically detect each successive Ri peak in the digital signaldelivered by the converter 30, and to automatically construct an RRseries (FIG. 4) consisting of a succession of RRi samples. Each RRisample is defined by the pair of coordinates: t [a sampling moment (ornumber)]; a time interval δti (expressed as a multiple of the samplingfrequency fc) separating a peak Ri from the next peak R_(i+1) (inanother embodiment it could be the previous peak R_(i−1)).

In the usual manner, the R wave usually being the finest and mostextensive part of the QRS, it is preferably used to detect heart beatwith very good accuracy, the time interval δti corresponding in practiceto the time between two successive heartbeats. However, in anothervariant, one might consider using other waves (such as Q wave or S wave)of a heart beat of the ECG signal to detect and construct the RR series.In another variant, one could also consider using other cardiac signalssuch as the plethysmograph waveform or the invasive blood pressure.

Step 2: Filtering the RR Series with Optional Automatic Detection ofIncorrect RR_(i) Samples and Replacement by RRc Reconstructed Samples

This filtering step consists generally in automatically detecting in theRR series the presence of one or more incorrect successive RR samples,and automatically replacing in the RR series the incorrect RR samplesthat were detected by reconstructed RRc samples. The number ofreconstructed RRc samples is, most of the time, different from thenumber of incorrect samples that were detected.

This filtering step with automatic reconstruction of incorrect RR_(i)samples is known per se, and examples of implementation of thisfiltering step are described for example in international patentapplication WO 02/069178, as well as in the article Logier R, DeJonckheere J, Dassonneville A., <<An efficient algorithm for R-Rintervals series filtering>>. Conf Proc IEEE Eng Med Biol Soc. 2004;6:3937-40.

It should however be noted that in the context of the invention, thedetection of incorrect RRi samples is not limited to the detectionmethods described in the two aforementioned publications, andreconstructed RRc samples can also be calculated in various ways, suchas, for example but not exclusively, by linear interpolation, asdescribed in the two abovementioned publications.

Each reconstructed RRc sample of the RR series is identified, forexample by an associated flag type identification variable. Thus, afterthis step, the RR series consists of RR_(i) samples some of which are,optionally, identified by their identification variable as reconstructedRRc samples.

Step 3: Resampling of the RR Series to a Predefined Frequency f toObtain Resampled RR_(i) Samples

The filtered RR series (FIG. 4) supplied by the aforementioned firstsub-module is automatically resampled by a second software sub-module ata predefined frequency f, which is preferably lower than the samplingfrequency fc (for example, for a sampling frequency fc equal to 250 Hz,the resampling frequency f will be set to 8 hz). The purpose of thisresampling is to output an RR series whose RR_(i) samples areequidistant from a temporal point of view, that is to say, in otherwords an RR series in which the sampling instants are regular. Thisresampling is carried out in known manner by interpolation, for exampleby linear interpolation.

During this resampling, each reconstructed RRc sample is replaced, asappropriate, by one or more reconstructed and resampled RRrc samples.

Each reconstructed and resampled RRrc sample of the RR series isidentified, for example by an associated flag type identificationvariable. Thus, after this step, the RR series consists of RRi samplessome of which are, optionally, identified by their identificationvariable as reconstructed and resampled RRrc samples.

Step 4: Selection of RRi Samples (of the RR Series, Optionally PartlyReconstructed and Sesampled) included in a Main Time Window of n Seconds(n>1/f)

This step consists in isolating a number N of successive RRi samples(N=n.f). As an indication, for example, a main window of 64 seconds(n=64) is chosen, which corresponds to 512 successive RR samples (N=512)at a resampling frequency f of 8 hz.

The following steps are applied to the samples included in this mainwindow.

Step 5: Calculation of a NivQual Quality Index

This step is performed using a software sub-module that automaticallycalculate a NivQual quality index significant of the quality of the RRseries.

In the particular embodiment described in detail below, this NivQualquality index has two quality levels of 0 or 1.

More particularly, the NivQual quality index is based on two variables(FC_(i); NORME) which are calculated in Step 5:

-   1/ the value of the instantaneous heart rate (FC_(i)) calculated on    each RRi sample of the RR series from Step 2, that is to say, the RR    series after filtering (optionally partly reconstructed) and before    resampling.-   2/ the mathematical norm value (NORME) of the RR_(i) samples of the    RR series (optionally partly reconstructed and resampled) from    selection Step 4 in the time window of n seconds.

The heart rate is defined by FC_(i)=60000/RR_(i), where RRi is theinstantaneous value of the RR_(i) sample in millisecond.

Calculating the mathematical norm value of the RR series resampled atthe frequency f n in the window of n seconds consists initially incalculating the average value M of RR in the window.

$M = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {RR}_{i} \right)}}$

where RR_(i) represents the value of each RR interval and N the numberof samples in the window.

This average value is then subtracted at each RR_(i) interval of thewindow.

RR _(i)=(RR _(i) −M).

The RR_(i) values obtained are used for the calculation of the normvalue (NORME), or:

${NORME} = \sqrt{\sum\limits_{i = 1}^{N}\left( {{RR}_{i} - {\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {RR}_{i} \right)}}} \right)^{2}}$

An example of algorithm for calculating the NivQual quality index fromthe two aforementioned variables (FC_(i); NORME) is given below:

If     ((NORME<NormMin)     or     (NORME>NormMax)     or    (FCi>FCMax)     or     (FC_(i)<FCMin) then Nivqual = 0 IF NOTNivqual = 1

The values of the FCMax, FCmin, NormMax, NormMin parameters arepredefined constants, which depend, for example, on the age of the humanbeing or depend, for example, on the animal species in the context of aveterinary application. The values of the FCMax, FCmin thresholds arethose commonly used by all heart monitoring devices. The values ofNormMax, NormMin thresholds of the norm value are, for example,experimentally determined on 200 individuals in each category.

By way of non-limiting example:

-   -   for a newborn: FCmax=250; FCMin=80; NormMax=3; NormMin=0    -   for an adult: FCMax=180; FCMin=30; NormMax=4; NormMin=0.07

The NivQual quality index calculated at each Step 5 may, for example, bedisplayed, especially in real time, so as to inform a practitioner ofthe quality level of the measured RR signal.

In the case of a NivQual quality index equal to 0, the RR series fromStep 1 is considered as being of very poor quality and in fact unusable.This lack of quality of the RR series may result from many factors, suchas, for example, and in a non-limiting and non-exhaustive manner,improper positioning of the electrodes 1 or the sensors for measuringthe heart signal, insufficient signal amplification in the signalprocessing chain, etc.

When calculating a NivQual quality index equal to 0, processing unit 31can be programmed to automatically trigger several actions, includingand not limited to, triggering of a visual and/or audible alarm, and/orresetting acquisition Step 1 of RR_(i) samples, including, inparticular, a manual or automatic gain change of the source signal(ECG).

In the context of the invention, for the implementation of Step 5, thisNivQual quality index calculation algorithm can be simplified by onlytaking into account the NORME parameter.

In another embodiment, the filtering Step 2 (detection andreconstruction of incorrect samples) may be omitted. In this case, thecalculation of the norm value (NORME) and calculation of theinstantaneous heart rate (FC_(i)) are performed in a sliding windowdirectly on the samples of the initial RR series from Step 1.

1. A method for diagnosing a cardiac condition of a subject based onquality control of an initial RR series comprised of a plurality ofsamples (RR_(i)) which are respectively a function of time intervals(δti) which separate two successive heartbeats, method during which oneresamples the RR series so as to obtain a resampled RR series,comprising automatically controlling the quality of the RR series byautomatically calculating at least the mathematical norm value (NORME),in a sliding window, of the resampled RR series, said mathematical normvalue being given by the following formula:${NORME} = \sqrt{\sum\limits_{i = 1}^{N}\left( {{RR}_{i} - {\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {RR}_{i} \right)}}} \right)^{2}}$where N is the number of RR_(i) samples in said window; and diagnosingthe cardiac condition of the subject based on the RR series.
 2. Themethod according to claim 1, during which one automatically detects inthe initial RR series if one or more successive (RRi) samples areincorrect, and automatically corrects, in the RR series, the one or more(RRi) samples detected as incorrect by replacing them by one or morereconstructed (RRc) samples, so as to obtain an RR series optionallypartly reconstructed, and wherein the resampling is performed on the RRseries, optionally partly reconstructed, so as to obtain an optionallypartly reconstructed and resampled RR series.
 3. The method according toclaim 1, wherein one automatically triggers an action when themathematical norm value (NORME) that is calculated is smaller than apredefined value (NormMin).
 4. The method according to claim 1, whereinone automatically triggers an action when the mathematical norm value(NORME) that is calculated is greater than a predefined value (NormMax).5. The method according to claim 1, wherein one automatically triggersan action when the mathematical norm value that is calculated is outsidea predefined range (NormMin; NormMax).
 6. The method according to claim1, wherein one automatically controls the quality of the RR series byalso calculating the instantaneous heart rate FC_(i), whereFC_(i)=60000/RR_(i), RR_(i) being the instantaneous value in millisecondof an (RRi) sample of the RR series.
 7. The method according to claim 6,wherein one automatically triggers an action when the instantaneousheart rate FC_(i) that is calculated is lower than a predefined value(FCMin).
 8. The method according to claim 6, wherein one automaticallytriggers an action when the instantaneous heart rate FC_(i) that iscalculated is greater than a predefined value (FCMax).
 9. The methodaccording to claim 6, wherein one automatically triggers an action whenthe instantaneous heart rate FC_(i) that is calculated is outside of apredefined range (FCMin, FCMax).
 10. The method according to claim 3,wherein the action that is triggered comprises the triggering of avisual and/or audible alarm.
 11. The method according to claim 3,wherein the action that is triggered comprises resetting the acquisitionand construction of the (RR_(i)) samples of the initial RR series. 12.The method according to claim 1, wherein one calculates a (NivQual)quality index from at least the mathematical norm value (NORME) that iscalculated.
 13. The method according to claim 1, wherein one calculatesat least one (NivQual) quality index from the instantaneous heart rateFC_(i), with FC_(i)=60000/RR_(i), RR_(i) being the instantaneous valuein millisecond of a (RRi) sample of the RR series.
 14. The methodaccording to claim 1, further comprising the acquisition andconstruction in real time of successive (RR_(i)) samples of the initialRR series from a cardiac signal, and wherein the resampling and theautomatic quality control of the RR series are performed in real timewhile said acquisition and construction of the successive (RR_(i))samples of the initial RR series are taking place.
 15. A processingdevice (3) of an RR series consisting of a plurality of (RRi) sampleswhich are respectively a function of time intervals (δti) separating twosuccessive heartbeats, said device (3) being arranged so as toautomatically control the quality of the RR series by implementing themethod described claim
 1. 16. An acquisition and processing system for acardiac signal, said system comprising electronic acquisition means(1,2) of a cardiac signal, and electronic processing means (3) arrangedso as to construct an initial RR series from the cardiac signal acquiredby the electronic acquisition means (1,2), said RR series consisting ofa plurality of (RRi) samples which are respectively a function of timeintervals (δti) which separate two successive heartbeats of the cardiacsignal, characterized in that said electronic processing means (3) arearranged so as to automatically control the quality of this RR series byimplementing the method referred to in claim
 1. 17. A computer programcomprising a computer program adapted to be executed by electronicprocessing means (3), and allowing, when executed by electronicprocessing means (3), to implement the quality control method of an RRseries referred to in claim 1.